Overview

Dataset statistics

Number of variables13
Number of observations113067
Missing cells0
Missing cells (%)0.0%
Duplicate rows10538
Duplicate rows (%)9.3%
Total size in memory8.5 MiB
Average record size in memory79.0 B

Variable types

Categorical4
Numeric9

Alerts

Dataset has 10538 (9.3%) duplicate rowsDuplicates
hour is highly overall correlated with cycle_Evening and 2 other fieldsHigh correlation
flights_per_hour is highly overall correlated with cycle_NightHigh correlation
cycle_Evening is highly overall correlated with hourHigh correlation
cycle_Morning is highly overall correlated with hourHigh correlation
cycle_Night is highly overall correlated with hour and 1 other fieldsHigh correlation
cycle_Night is highly imbalanced (71.3%)Imbalance
prcp has 77039 (68.1%) zerosZeros
snwd has 106237 (94.0%) zerosZeros

Reproduction

Analysis started2023-06-15 11:37:41.154036
Analysis finished2023-06-15 11:38:00.344484
Duration19.19 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

delayed_flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size883.5 KiB
0
85167 
1
27900 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters113067
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 85167
75.3%
1 27900
 
24.7%

Length

2023-06-15T12:38:00.446315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T12:38:00.606214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 85167
75.3%
1 27900
 
24.7%

Most occurring characters

ValueCountFrequency (%)
0 85167
75.3%
1 27900
 
24.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 113067
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 85167
75.3%
1 27900
 
24.7%

Most occurring scripts

ValueCountFrequency (%)
Common 113067
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 85167
75.3%
1 27900
 
24.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113067
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 85167
75.3%
1 27900
 
24.7%

hour
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.167158
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size883.5 KiB
2023-06-15T12:38:00.751111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q19
median13
Q317
95-th percentile21
Maximum23
Range22
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.7687551
Coefficient of variation (CV)0.36217043
Kurtosis-1.2424752
Mean13.167158
Median Absolute Deviation (MAD)4
Skewness-0.0051237951
Sum1488771
Variance22.741025
MonotonicityNot monotonic
2023-06-15T12:38:00.910419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
6 9268
 
8.2%
8 9013
 
8.0%
15 8139
 
7.2%
17 7870
 
7.0%
16 7512
 
6.6%
7 7204
 
6.4%
19 7033
 
6.2%
14 6825
 
6.0%
20 6690
 
5.9%
12 6605
 
5.8%
Other values (10) 36908
32.6%
ValueCountFrequency (%)
1 4
 
< 0.1%
5 1537
 
1.4%
6 9268
8.2%
7 7204
6.4%
8 9013
8.0%
9 6559
5.8%
10 5095
4.5%
11 6220
5.5%
12 6605
5.8%
13 5300
4.7%
ValueCountFrequency (%)
23 44
 
< 0.1%
22 744
 
0.7%
21 4889
4.3%
20 6690
5.9%
19 7033
6.2%
18 6516
5.8%
17 7870
7.0%
16 7512
6.6%
15 8139
7.2%
14 6825
6.0%

visib
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3752437
Minimum0.13
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size883.5 KiB
2023-06-15T12:38:01.068608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile4
Q110
median10
Q310
95-th percentile10
Maximum10
Range9.87
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.8200457
Coefficient of variation (CV)0.19413316
Kurtosis8.5394575
Mean9.3752437
Median Absolute Deviation (MAD)0
Skewness-3.069539
Sum1060030.7
Variance3.3125664
MonotonicityNot monotonic
2023-06-15T12:38:01.223503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
10 97423
86.2%
9 2596
 
2.3%
4 2062
 
1.8%
8 2001
 
1.8%
6 1903
 
1.7%
5 1771
 
1.6%
3 1397
 
1.2%
7 1314
 
1.2%
2.5 678
 
0.6%
2 618
 
0.5%
Other values (10) 1304
 
1.2%
ValueCountFrequency (%)
0.13 13
 
< 0.1%
0.24 1
 
< 0.1%
0.25 42
 
< 0.1%
0.5 175
 
0.2%
0.75 144
 
0.1%
1 278
0.2%
1.25 109
 
0.1%
1.5 352
0.3%
1.75 130
 
0.1%
2 618
0.5%
ValueCountFrequency (%)
10 97423
86.2%
9 2596
 
2.3%
8 2001
 
1.8%
7 1314
 
1.2%
6 1903
 
1.7%
5 1771
 
1.6%
4 2062
 
1.8%
3.5 60
 
0.1%
3 1397
 
1.2%
2.5 678
 
0.6%

prcp
Real number (ℝ)

Distinct60
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11938629
Minimum0
Maximum3.86
Zeros77039
Zeros (%)68.1%
Negative0
Negative (%)0.0%
Memory size883.5 KiB
2023-06-15T12:38:01.412573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.04
95-th percentile0.7
Maximum3.86
Range3.86
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.37186433
Coefficient of variation (CV)3.1147992
Kurtosis39.033874
Mean0.11938629
Median Absolute Deviation (MAD)0
Skewness5.5133376
Sum13498.65
Variance0.13828308
MonotonicityNot monotonic
2023-06-15T12:38:01.618577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 77039
68.1%
0.05 3180
 
2.8%
0.01 2877
 
2.5%
0.03 2751
 
2.4%
0.04 2533
 
2.2%
0.1 1700
 
1.5%
0.09 1304
 
1.2%
0.02 1277
 
1.1%
0.15 1020
 
0.9%
0.14 969
 
0.9%
Other values (50) 18417
 
16.3%
ValueCountFrequency (%)
0 77039
68.1%
0.01 2877
 
2.5%
0.02 1277
 
1.1%
0.03 2751
 
2.4%
0.04 2533
 
2.2%
0.05 3180
 
2.8%
0.06 298
 
0.3%
0.08 338
 
0.3%
0.09 1304
 
1.2%
0.1 1700
 
1.5%
ValueCountFrequency (%)
3.86 263
0.2%
3.05 305
0.3%
2.15 332
0.3%
2.06 318
0.3%
1.79 259
0.2%
1.56 269
0.2%
1.51 269
0.2%
1.24 557
0.5%
1.22 324
0.3%
1.15 5
 
< 0.1%

pres
Real number (ℝ)

Distinct194
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1017.1589
Minimum995.8
Maximum1039.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size883.5 KiB
2023-06-15T12:38:01.857527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum995.8
5-th percentile1006.3
Q11013.2
median1016.3
Q31021.3
95-th percentile1029
Maximum1039.1
Range43.3
Interquartile range (IQR)8.1

Descriptive statistics

Standard deviation6.8609977
Coefficient of variation (CV)0.0067452563
Kurtosis0.59565993
Mean1017.1589
Median Absolute Deviation (MAD)3.9
Skewness0.13200232
Sum1.1500711 × 108
Variance47.073289
MonotonicityNot monotonic
2023-06-15T12:38:02.055521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1015.3 2805
 
2.5%
1014.2 2624
 
2.3%
1016.1 2003
 
1.8%
1017.7 1993
 
1.8%
1013.2 1939
 
1.7%
1014.6 1921
 
1.7%
1017.3 1858
 
1.6%
1011.8 1625
 
1.4%
1015.8 1597
 
1.4%
1014.7 1507
 
1.3%
Other values (184) 93195
82.4%
ValueCountFrequency (%)
995.8 593
0.5%
997.5 319
0.3%
998.4 307
0.3%
1001.9 334
0.3%
1002.2 327
0.3%
1002.6 315
0.3%
1002.8 330
0.3%
1002.9 299
0.3%
1003.4 619
0.5%
1003.7 319
0.3%
ValueCountFrequency (%)
1039.1 329
0.3%
1036.1 296
0.3%
1035.4 219
0.2%
1035.2 319
0.3%
1034.1 338
0.3%
1033.9 235
0.2%
1033.6 306
0.3%
1033.4 322
0.3%
1033.2 230
0.2%
1032.4 324
0.3%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5809387
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size883.5 KiB
2023-06-15T12:38:02.222409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.411402
Coefficient of variation (CV)0.51837619
Kurtosis-1.1788844
Mean6.5809387
Median Absolute Deviation (MAD)3
Skewness-0.022438548
Sum744087
Variance11.637663
MonotonicityNot monotonic
2023-06-15T12:38:02.359474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 10264
9.1%
5 9821
8.7%
10 9731
8.6%
7 9686
8.6%
4 9652
8.5%
6 9630
8.5%
12 9583
8.5%
11 9419
8.3%
3 9388
8.3%
1 9086
8.0%
Other values (2) 16807
14.9%
ValueCountFrequency (%)
1 9086
8.0%
2 7964
7.0%
3 9388
8.3%
4 9652
8.5%
5 9821
8.7%
6 9630
8.5%
7 9686
8.6%
8 10264
9.1%
9 8843
7.8%
10 9731
8.6%
ValueCountFrequency (%)
12 9583
8.5%
11 9419
8.3%
10 9731
8.6%
9 8843
7.8%
8 10264
9.1%
7 9686
8.6%
6 9630
8.5%
5 9821
8.7%
4 9652
8.5%
3 9388
8.3%

flights_per_day
Real number (ℝ)

Distinct178
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean841.00111
Minimum535
Maximum931
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size883.5 KiB
2023-06-15T12:38:02.693438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum535
5-th percentile645
Q1820
median863
Q3899
95-th percentile917
Maximum931
Range396
Interquartile range (IQR)79

Descriptive statistics

Standard deviation81.433341
Coefficient of variation (CV)0.096829054
Kurtosis1.7665901
Mean841.00111
Median Absolute Deviation (MAD)38
Skewness-1.5696338
Sum95089472
Variance6631.3891
MonotonicityNot monotonic
2023-06-15T12:38:02.893304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
913 2386
 
2.1%
905 2069
 
1.8%
915 2025
 
1.8%
911 2013
 
1.8%
849 1954
 
1.7%
854 1949
 
1.7%
908 1724
 
1.5%
899 1698
 
1.5%
892 1659
 
1.5%
912 1648
 
1.5%
Other values (168) 93942
83.1%
ValueCountFrequency (%)
535 204
0.2%
580 219
0.2%
584 214
0.2%
590 219
0.2%
592 218
0.2%
595 211
0.2%
597 237
0.2%
600 221
0.2%
602 228
0.2%
606 217
0.2%
ValueCountFrequency (%)
931 361
 
0.3%
928 357
 
0.3%
927 683
0.6%
925 360
 
0.3%
924 322
 
0.3%
923 1004
0.9%
922 339
 
0.3%
921 353
 
0.3%
918 697
0.6%
917 1312
1.2%

flights_per_hour
Real number (ℝ)

Distinct82
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.112694
Minimum2
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size883.5 KiB
2023-06-15T12:38:03.115490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile31
Q145
median54
Q360
95-th percentile69
Maximum84
Range82
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.495151
Coefficient of variation (CV)0.23977173
Kurtosis1.4690508
Mean52.112694
Median Absolute Deviation (MAD)7
Skewness-0.87173275
Sum5892226
Variance156.12879
MonotonicityNot monotonic
2023-06-15T12:38:03.307493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 5668
 
5.0%
58 5166
 
4.6%
57 5039
 
4.5%
60 4780
 
4.2%
56 4392
 
3.9%
55 3831
 
3.4%
50 3668
 
3.2%
61 3589
 
3.2%
62 3526
 
3.1%
54 3498
 
3.1%
Other values (72) 69910
61.8%
ValueCountFrequency (%)
2 14
 
< 0.1%
3 30
 
< 0.1%
4 43
 
< 0.1%
5 76
 
0.1%
6 60
 
0.1%
7 101
 
0.1%
8 367
0.3%
9 291
0.3%
10 362
0.3%
11 204
0.2%
ValueCountFrequency (%)
84 2
 
< 0.1%
82 115
 
0.1%
81 91
 
0.1%
80 235
0.2%
79 231
0.2%
78 416
0.4%
77 157
 
0.1%
76 155
 
0.1%
75 295
0.3%
74 485
0.4%

snwd
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16737775
Minimum0
Maximum5.9
Zeros106237
Zeros (%)94.0%
Negative0
Negative (%)0.0%
Memory size883.5 KiB
2023-06-15T12:38:03.459590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.2
Maximum5.9
Range5.9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7619903
Coefficient of variation (CV)4.5525185
Kurtosis28.785349
Mean0.16737775
Median Absolute Deviation (MAD)0
Skewness5.2523547
Sum18924.9
Variance0.58062922
MonotonicityNot monotonic
2023-06-15T12:38:03.582502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 106237
94.0%
1.2 2302
 
2.0%
2 1313
 
1.2%
3.1 1167
 
1.0%
5.1 1088
 
1.0%
3.9 647
 
0.6%
5.9 313
 
0.3%
ValueCountFrequency (%)
0 106237
94.0%
1.2 2302
 
2.0%
2 1313
 
1.2%
3.1 1167
 
1.0%
3.9 647
 
0.6%
5.1 1088
 
1.0%
5.9 313
 
0.3%
ValueCountFrequency (%)
5.9 313
 
0.3%
5.1 1088
 
1.0%
3.9 647
 
0.6%
3.1 1167
 
1.0%
2 1313
 
1.2%
1.2 2302
 
2.0%
0 106237
94.0%

tavg
Real number (ℝ)

Distinct68
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.018883
Minimum16
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size883.5 KiB
2023-06-15T12:38:03.765507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile27
Q143
median58
Q372
95-th percentile80
Maximum87
Range71
Interquartile range (IQR)29

Descriptive statistics

Standard deviation16.816694
Coefficient of variation (CV)0.29493202
Kurtosis-0.90074728
Mean57.018883
Median Absolute Deviation (MAD)15
Skewness-0.28981001
Sum6446954
Variance282.8012
MonotonicityNot monotonic
2023-06-15T12:38:03.962505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76 4496
 
4.0%
78 3963
 
3.5%
70 3832
 
3.4%
42 3774
 
3.3%
54 3628
 
3.2%
75 3623
 
3.2%
71 3345
 
3.0%
43 3206
 
2.8%
67 3203
 
2.8%
63 3156
 
2.8%
Other values (58) 76841
68.0%
ValueCountFrequency (%)
16 322
 
0.3%
17 578
 
0.5%
18 329
 
0.3%
19 281
 
0.2%
20 268
 
0.2%
23 324
 
0.3%
24 1682
1.5%
25 265
 
0.2%
26 312
 
0.3%
27 1453
1.3%
ValueCountFrequency (%)
87 664
 
0.6%
85 936
 
0.8%
84 990
 
0.9%
83 664
 
0.6%
82 576
 
0.5%
81 944
 
0.8%
80 1914
1.7%
79 1184
 
1.0%
78 3963
3.5%
77 2607
2.3%

cycle_Evening
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size883.5 KiB
0
84958 
1
28109 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters113067
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 84958
75.1%
1 28109
 
24.9%

Length

2023-06-15T12:38:04.147442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T12:38:04.297496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 84958
75.1%
1 28109
 
24.9%

Most occurring characters

ValueCountFrequency (%)
0 84958
75.1%
1 28109
 
24.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 113067
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 84958
75.1%
1 28109
 
24.9%

Most occurring scripts

ValueCountFrequency (%)
Common 113067
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 84958
75.1%
1 28109
 
24.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113067
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 84958
75.1%
1 28109
 
24.9%

cycle_Morning
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size883.5 KiB
0
68171 
1
44896 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters113067
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 68171
60.3%
1 44896
39.7%

Length

2023-06-15T12:38:04.421476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T12:38:04.571491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 68171
60.3%
1 44896
39.7%

Most occurring characters

ValueCountFrequency (%)
0 68171
60.3%
1 44896
39.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 113067
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 68171
60.3%
1 44896
39.7%

Most occurring scripts

ValueCountFrequency (%)
Common 113067
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 68171
60.3%
1 44896
39.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113067
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 68171
60.3%
1 44896
39.7%

cycle_Night
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size883.5 KiB
0
107386 
1
 
5681

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters113067
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 107386
95.0%
1 5681
 
5.0%

Length

2023-06-15T12:38:04.694405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T12:38:04.844298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 107386
95.0%
1 5681
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 107386
95.0%
1 5681
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 113067
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 107386
95.0%
1 5681
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common 113067
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 107386
95.0%
1 5681
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113067
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 107386
95.0%
1 5681
 
5.0%

Interactions

2023-06-15T12:37:57.852489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:44.694541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:46.417678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:47.979570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:49.649499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:51.177456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:52.735474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:54.657587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:56.228519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:58.025532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:44.972346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:46.585566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:48.154794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:49.804402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:51.345542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:52.918536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:54.824545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:56.390630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:58.199451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:45.138229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:46.761571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:48.332610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:49.967700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:51.508528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:53.106414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:54.999609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:56.558678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:58.394609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:45.330095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:46.950570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:48.531578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:50.149549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:51.694516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:53.314619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:55.190547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:56.746501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:58.563644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:45.493977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:47.113673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:48.704546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:50.309520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:51.854750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:53.504491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:55.357438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:56.909477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:58.737527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:45.658861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:47.279629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:48.892659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:50.473409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:52.019560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:53.696623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:55.523528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:57.081362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:58.933518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:45.871724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:47.467655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:49.096603image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:50.660615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:52.207641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:53.905629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:55.721547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:57.293611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:59.100601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:46.038606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:47.631685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:49.277542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:50.818513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:52.371532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:54.091570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:55.880347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:57.470632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:59.280588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:46.227685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:47.800692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:49.458630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:50.992584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:52.545596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:54.449576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:56.052504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-15T12:37:57.656496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-06-15T12:38:04.964492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
hourvisibprcppresmonthflights_per_dayflights_per_hoursnwdtavgdelayed_flagcycle_Eveningcycle_Morningcycle_Night
hour1.000-0.069-0.0110.0040.007-0.006-0.2890.0030.0010.3351.0000.9201.000
visib-0.0691.000-0.2590.1800.1040.0710.044-0.0060.0390.0940.0750.0740.032
prcp-0.011-0.2591.000-0.170-0.062-0.028-0.018-0.0750.0200.1030.0000.0110.008
pres0.0040.180-0.1701.0000.115-0.092-0.0290.054-0.2420.0830.0000.0000.015
month0.0070.104-0.0620.1151.0000.014-0.014-0.1730.2010.0830.0150.0210.050
flights_per_day-0.0060.071-0.028-0.0920.0141.0000.360-0.1100.3190.0880.0250.0270.018
flights_per_hour-0.2890.044-0.018-0.029-0.0140.3601.000-0.0310.0600.1030.2790.4060.524
snwd0.003-0.006-0.0750.054-0.173-0.110-0.0311.000-0.3660.0660.0060.0040.012
tavg0.0010.0390.020-0.2420.2010.3190.060-0.3661.0000.0780.0040.0160.023
delayed_flag0.3350.0940.1030.0830.0830.0880.1030.0660.0781.0000.2500.2700.109
cycle_Evening1.0000.0750.0000.0000.0150.0250.2790.0060.0040.2501.0000.4670.132
cycle_Morning0.9200.0740.0110.0000.0210.0270.4060.0040.0160.2700.4671.0000.187
cycle_Night1.0000.0320.0080.0150.0500.0180.5240.0120.0230.1090.1320.1871.000

Missing values

2023-06-15T12:37:59.548405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-15T12:37:59.969540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

delayed_flaghourvisibprcppresmonthflights_per_dayflights_per_hoursnwdtavgcycle_Eveningcycle_Morningcycle_Night
012110.00.01027.410811340.058001
10510.00.01027.41081130.058010
20510.00.01027.41081130.058010
30610.00.01027.410811380.058010
40610.00.01027.410811380.058010
50610.00.01027.410811380.058010
60610.00.01027.410811380.058010
70610.00.01027.410811380.058010
80610.00.01027.410811380.058010
90610.00.01027.410811380.058010
delayed_flaghourvisibprcppresmonthflights_per_dayflights_per_hoursnwdtavgcycle_Eveningcycle_Morningcycle_Night
11305702110.00.01020.59607160.061001
11305802110.00.01020.59607160.061001
11305902110.00.01020.59607160.061001
11306002110.00.01020.59607160.061001
11306102110.00.01020.59607160.061001
11306202110.00.01020.59607160.061001
11306302110.00.01020.59607160.061001
11306402210.00.01020.5960780.061001
11306502110.00.01020.59607160.061001
11306602110.00.01020.59607160.061001

Duplicate rows

Most frequently occurring

delayed_flaghourvisibprcppresmonthflights_per_dayflights_per_hoursnwdtavgcycle_Eveningcycle_Morningcycle_Night# duplicates
4130610.00.001010.07915680.07801036
6950610.00.701013.68909680.08001036
5450610.00.001021.58912680.07501035
7080610.02.151008.97889680.07301035
4280610.00.001012.67905680.07101034
4340610.00.001013.28927690.07401034
5200610.00.001018.78925680.07101034
5280610.00.001019.88911690.07801034
5400610.00.001020.98928680.07801034
5440610.00.001021.47878670.07701034